docstr: Feature-level and instance-level outlier scores.
  The scores are equal to the negative likelihood ratios.
docstr_tok: ["Feature-level","and","instance-level","outlier","scores",".","The","scores","are","equal","to","the","negative","likelihood","ratios","."]
code: def score(
      self, X: np.ndarray, batch_size: int = int(1e10)
  ) -> Tuple[np.ndarray, np.ndarray]:
      """
      Feature-level and instance-level outlier scores.
      The scores are equal to the negative likelihood ratios.
      """
      fscore = self.feature_score(X, batch_size=batch_size)
      iscore = self.instance_score(X, batch_size=batch_size)
      return fscore, iscore
  
imports: from functools import partial
  import logging
  import numpy
  import tensorflow
  from tensorflow.keras.layers import Input
  from tensorflow.keras.models import Model
  from tensorflow_probability.python.distributions.distribution import Distribution
  from typing import Callable, Dict, Tuple, Union
  from alibi_detect.models.tensorflow.pixelcnn import PixelCNN
  from alibi_detect.models.tensorflow.trainer import trainer
  from alibi_detect.base import BaseDetector, FitMixin, ThresholdMixin, outlier_prediction_dict
  from alibi_detect.utils.tensorflow.prediction import predict_batch
  from alibi_detect.utils.tensorflow.perturbation import mutate_categorical
  from alibi_detect.utils._types import OptimizerTF
label_desc: Use image node_id 9 for calling the LLR obj's underlying member method code with example usage: obj.score(X, batch_size) and returns: fscore,  iscore